不同前车车型对后车前端进气的影响

针对尾随行驶过程中前车对后车发动机舱冷却性能产生影响的问题,以轿车、皮卡车、面包车和城市公交客车作为前车车型,以轿车作为后车车型,分析不同车距下后车发动机舱进气质量流量比以及冷凝器入口的速度分布和流场.结果表明,当前车为轿车时对后车前端进气影响较小;当前车为皮卡车时后车前端进气随着车距的增加会先减少后增多,在车距为1倍后车车长时减少约7%;当前车为面包车或城市公交客车时对后车发动机舱的进气影响的变化规律一致,当车距为0.2倍后车车长时减少约13%.     

Monitoring vehicle outliers based on clustering technique

In this paper, we develop a novel framework, called Monitoring Vehicle Outliers based on a Clustering technique (MVOC), for monitoring vehicle outliers caused by complex vehicle states. The vehicle outlier monitoring is a method to continuously check the current vehicle conditions. Most of previous monitoring methods have conducted simple operations depending on uncomplicated analyses or expected lifetimes in regard to vehicle components. However, many serious vehicle outliers such as turning off during a drive result from the complex vehicle states influenced by correlated components. The proposed method monitors the current vehicle conditions based on not simple components like the previous methods but more complex and various vehicle states using a clustering technique. We perform vehicle data clustering and then analyze the generated clusters with information of vehicle outliers caused by complex correlations of vehicle components. Thus, we can learn vehicle information in more detail. To facilitate MVOC, we also propose related techniques such as sampling cluster data with representative attributes and deciding cluster characteristics on the basis of relations between vehicle data and states. Then, we demonstrate the performance of our approach in terms of monitoring vehicle outliers on the basis of real complex correlations between outliers and vehicle data through various experiments. Experimental results show that the proposed method can not only monitor the complex outliers by predicting their occurrence possibilities in advance but also outperform a standard technique. Moreover, we present statistical significance of the results through significance tests.     

A novel particle filter implementation for a multiple-vehicle detection and tracking system using tail light segmentation

This paper proposes a vision-based multiple vehicle automatic detection and tracking system which can be applied in different environments. To detect vehicles, tail light position is utilized for fast vehicle candidate localization. A back propagation neural network (BPNN) trained by a Gabor feature set is used. BPNN verifies vehicle candidates and ensures detection system robustness. In the vehicle tracking step, to overcome multiple vehicle tracking challenges, partial vehicle occlusion and temporarily missing vehicle problems, this paper propose a novel method implementing a particle filter. The color probability distribution function (CPDF) of detected vehicles is used twice in the vehicle tracking sub-system. Firstly, CPDF is adopted to seek potential target vehicle locations; secondly, CPDF is used to measure the similarity of each particle for target vehicle position estimation. Because of various illuminations or target vehicle distances, the same vehicle will generate different CPDFs; the initial CPDF cannot guarantee long-term different scale vehicle tracking. To overcome these problems, an accurate tracking result, which is chosen by a trained BPNN, is used to update target vehicle CPDF. In our experiments, the proposed algorithm showed 84% accuracy in vehicle detection. Videos collected from highways, urban roads and campuses are tested in our system. The system performance makes it appropriate for real applications.     

基于四旋翼无人机的车辆自主抄牌

针对传统车辆抄牌效率低的问题,提出一种基于四旋翼无人机的车辆自主抄牌方法。使用无人机采集大量车辆俯视图样本,对图像进行预处理和下采样,然后提取车辆图像的梯度直方图特征,将图像特征输入到卷积神经网络中,训练出车辆识别模型,最后使用模型识别车辆;根据车辆的形状特征估计车辆姿态;根据车辆的位姿信息计算无人机抄牌的位置和角度;搭建无人机实验平台测试无人机自主抄牌系统。实验结果表明无人机可以自主拍摄到清晰的车牌图像,实现了车辆抄牌的自动化。     

A general heuristic for vehicle routing problems

We present a unified heuristic which is able to solve five different variants of the vehicle routing problem: the vehicle routing problem with time windows (VRPTW), the capacitated vehicle routing problem (CVRP), the multi-depot vehicle routing problem (MDVRP), the site-dependent vehicle routing problem (SDVRP) and the open vehicle routing problem (OVRP).     

从安全角度评述汽车操作系统

汽车操作系统是连接和管理汽车电子电气系统中软、硬件的重要组件,具有极强的领域性。当前,汽车电子电气系统结构正在经历一场革命,汽车操作系统也将随之发生巨大变化。然而,无论如何变化,汽车安全始终是汽车的第一属性。本文立足于汽车安全的角度评述当前汽车操作系统:第一,概要地介绍汽车安全的两个分类;第二,概述汽车操作系统的特点,并讨论汽车操作系统维护汽车安全的主要方法;第三,介绍新一代汽车电子电气系统的变化,并从汽车安全出发,分析这些变化为何使当前汽车操作系统难以维护汽车安全;最后,指出汽车操作系统可能的研究方向。     

气垫车辆静垫升稳定性分析

静垫升稳定性是气垫车辆的关键特性之一,为了得到影响气垫车辆静垫升稳定性的主要因素,为气垫车辆实际设计与改装提供依据,针对BJ2032ZLF1车型所改装的气垫车辆,使用大型工程软件ADAMS和ANSYS建立了地面沉陷模型、气垫车辆多体动力学模型和流场模型,并对气垫车辆的静垫升过程进行了仿真,分析了气垫车辆在不同整车几何参数与力学参数影响下的静垫升稳定性,得到了气垫车辆能否平稳垫升取决于车辆改装后的重量分布、地面沉陷情况和车底气压中心位置的结论。     

Vehicle Dynamic State Estimation: State of the Art Schemes and Perspectives

Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.      

空气流动阻力下非线性车辆队列最优能耗控制方法

为了优化车辆队列在长距离行驶过程中的能源消耗,对空气流动阻力下车辆队列能耗优化间距策略以及相应的队列控制方法进行了研究;首先根据车辆队列在行驶过程中受到的空气流动阻力,建立基于异构风阻系数的车辆动力学模型;其次,设计基于滑模控制的非线性车辆队列控制方法,使其能够在不同风阻系数下稳定地收敛到期望的车辆队列;在此基础上,构建稳态下车辆队列能量消耗评价模型,并通过优化分析,计算能量消耗最优下的车辆队列期望车间距;最后通过数值仿真的手段验证所提控制方法的有效性与可行性;该结果表明:所设计的控制器能够使整个车辆队列达到期望的控制效果;得到的最优车间距能够使得特定条件下车辆队列稳态能量消耗降低。     

山区道路车辆动力学模型与行车安全分析

针对道路的几何线形,特别是纵坡坡度与弯道半径对车辆行驶状态的影响,建立了车路耦合的8自由度山区道路行驶的车辆动力学模型以及Dugoff轮胎力模型.结合车载GPS/IMU的测量信息,解算了不同车轮的滑移率以及垂直载荷,并通过横向载荷转移率(LLTR)对车辆的行驶稳定性进行分析.结果表明:车辆行驶过程中的侧向加速度与道路纵坡坡度以及车辆重心高度与宽度的比率h/T有关,坡度越陡,h/T越大,侧向加速度越大,车辆的行驶稳定性越差,降低车辆的行驶速度与侧向加速度可提高车辆的行驶稳定性.     

基于汽车爆胎时主动安全控制仿真系统设计研究

设计了一种爆胎应急主动安全制动系统,根据爆胎产生的附加横摆力矩建立了模糊控制模型的,针对发生爆胎的车辆通过自动制动将车速降低至安全范围直至车辆停住。辅助爆胎车辆实现安全停车,以防车辆失控,主动安全控制系统以实际爆胎及车辆运动状况为依据通过向车辆施加平衡力矩实现运动轨迹的纠偏控制过程。系统装置的模拟试验结果表明这种主动安全控制策略的可行性,它可确保爆胎车辆在较短时间内减速至安全车速并按原轨迹行驶,以确保爆胎车辆行驶的稳定性。     

基于计算机视觉技术的车辆远程控制技术研究

随着信息技术的发展,车辆远程控制成为了学术界的前瞻性研究。此次研究在合理利用计算机视觉技术的基础上,结合模糊PID控制智能算法建立了车辆远程控制系统,以期实现对车辆装置的实时监控,能够在危险状况发生时通过远程控制中心操控车辆。实验中,以履带车辆作为研究对象,对其相关参数进行了设计,以达到验证该控制系统有效性的目的。研究表明,无论是在直线行驶作业下还是曲线行驶作业下,相较于基于模糊PID控制算法的车辆控制系统,PID车辆控制系统均调节震荡大、调节时间长;而基于BP神经网络算法的车辆控制系统也存在诸多问题;因而基于模糊PID控制算法的车辆控制系统调控精度较高,适用性较强。此次研究对车辆远程控制研究具有一定的指导价值。     

神经网络算法在物流配送车辆优化调度中应用

研究物流配送车辆调度优化问题,车辆调度存在空驶率,运输路径不合理。为了有效节约车辆运输成本,优化城市车辆调度,传统的调度算法存在计算复杂度高,不利于实际应用等问题,提出了一种改进的神经网络车辆调度优化算法模型。首先对城市车辆调度建立优化数学模型,建立了一种解决非满载车辆卸货路线优化的神经网络模型,采用改进的神经网络进行优化车辆调度,并给出了解决配送车辆优化调度问题的具体步骤。仿真结果表明,提出的改进的算法不仅能有效地求解车辆调度优化模型,而且计算机复杂度较低,算法的计算效率较高,收敛速度较快,验证了改进算法的实用性和有效性。     

基于Android系统的智能循迹避障小车设计

智能循迹避障小车由控制系统Android设备和小车主体两部分构成.小车通过5路红外对管组成的循迹模块进行路径识别,通过3路超声波模块检测小车周边的障碍物,单片机(MCU)根据采集的环境信息控制小车按照正确的轨迹移动.采用蓝牙模块与上位机Android系统进行通信,采集的小车状态信息通过蓝牙模块上传至Android设备显示,Android设备通过蓝牙模块发送指令到单片机串口控制小车.智能循迹避障小车的研究在无人驾驶方面具有广泛的应用前景.     

A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways

Real-time highway traffic monitoring systems play a vital role in road traffic management, planning, and preventing frequent traffic jams, traffic rule violations, and fatal road accidents. These systems rely entirely on online traffic flow info estimated from time-dependent vehicle trajectories. Vehicle trajectories are extracted from vehicle detection and tracking data obtained by processing road-side camera images. General-purpose object detectors including Yolo, SSD, EfficientNet have been utilized extensively for real-time object detection task, but, in principle, Yolo is preferred because it provides a high frame per second (FPS) performance and robust object localization functionality. However, this algorithm’s average vehicle classification accuracy is below 57%, which is insufficient for traffic flow monitoring. This study proposes improving the vehicle classification accuracy of Yolo, and developing a novel bounding box (Bbox)-based vehicle tracking algorithm. For this purpose, a new vehicle dataset is prepared by annotating 7216 images with 123831 object patterns collected from highway videos. Nine machine learning-based classifiers and a CNN-based classifier were selected. Next, the classifiers were trained via the dataset. One out of ten classifiers with the highest accuracy was selected to combine to Yolo. This way, the classification accuracy of the Yolo-based vehicle detector was increased from 57% to 95.45%. Vehicle detector 1 (Yolo) and vehicle detector 2 (Yolo + best classifier), and the Kalman filter-based tracking as vehicle tracker 1 and the Bbox-based tracking as vehicle tracker 2 were applied to the categorical/total vehicle counting tasks on 4 highway videos. The vehicle counting results show that the vehicle counting accuracy of the developed approach (vehicle detector 2 + vehicle tracker 2) was improved by 13.25% and this method performed better than the other 3 vehicle counting systems implemented in this study.     

一种简单快速的车标定位方法

由于视点的不同,单纯的车辆外形对车辆的类型识别不具有决定性的意义,而车标则对车辆类型具有决定意义。提出了一种快速的从粗到精的车标定位方法：首先根据车头前方车牌的纹理特征大致确定车牌位置,并结合车头本身的对称性等先验知识粗略地确定车标的位置;然后在粗定位的小范围内利用边缘特征和形态运算进行车标的定位。考虑到各种噪声以及形态学的影响,在车标识别中利用模板匹配进行精定位和粗识别。利用已有的识别方法对该车标定位算法进行了验证,结果表明,该方法能快速、准确地定位车标位置,且识别率能达到实时应用的要求。     

基于散热器栅格背景精确分类的车标定位方法

由于车标的背景散热器栅格形状大小不一、颜色不定、背景多样,因此导致了车标定位的困难,故精确分类散热器栅格是准确定位车标的基础。提出了一种基于散热器栅格背景精确分类的车标定位方法,首先依照车牌与车标空间位置关系确定车标粗定位,然后依据栅格纹理特征,利用霍夫变换和灰度值的梯度变化确定散热器栅格背景的类别,进而通过不同算子分别对不同种类栅格背景进行背景消融;为了保证多种光照条件下的准确定位,引入离散度,并将其与大津法进行融合,形成一种适用于车标定位的自适应二值化方法,同时结合形态学对栅格背景进一步处理,得到准确的车标定位。这种方法适用于在不同光照强度和不同类型的车标背景条件下,对车标进行定位。对10类车标、30类散热器栅格共1 200张图像进行车标定位,实验结果显示,图像总体的车标定位准确率可以达到97.67%。     

基于非局部注意力和局部特征的车辆重识别算法

车辆重识别是指从不同的摄像机来重新识别出同一辆车。车辆重识别非常容易受到车辆角度以及光照等其他因素的影响,是一项非常有挑战性的任务。许多车辆重识别方法都过分关注车辆全局特征,而忽略了车辆图像的局部有分辨力的特征,造成了车辆重识别精度不高的问题。针对这一问题,本文提出一种整合非局部注意力的和多尺度特征的车辆重识别方法,使用注意力机制获取车辆显著特征,并融合多尺度特征从而提高车辆重识别的检索精度。首先,使用骨干特征提取网络与注意力模块获取车辆的显著性细粒度特征。然后,将特征分为多个分支进行度量学习,分别学习车辆的局部与全局特征,将全局特征与细粒度的局部特征融合,构建车辆重识别的特征。最后,利用该方法提取不同车辆的特征,计算不同车辆的相似度,从而判断是否具有相同的身份。实验结果表明本文提出的车辆重识别算法具有更高的精度。     

复合材料磁悬浮列车车体结构数值模拟(III)——车体结构性能的参数化数值模拟

在磁悬浮列车车体参数化数值模型的基础上,开展参数变化对车体结构性能影响的数值试验,研究复合材料梁截面几何参数对车体刚度和频率的影响。在典型荷载工况下,研究关键设计参数对车体结构性能、结构部件连接模型的力学性能、车体频率和振型、车体结构线性屈曲性能的影响,确定关键设计参数对复合材料车体结构性能的影响趋势,为车体优化设计奠定基础,验证将参数化车体数值模型作为车体结构性能研究的有效性。     

基于小波变换的车辆牌照定位研究

车辆牌照的准确定位是智能交通中车辆牌照识别技术的关键,提出一种基于小波变换的车牌质心定位方法,该方法可以很好地解决复杂背景与光照下的车牌定位.经过小波分析的车牌图像利用数学形态学进行车牌特征提取,对特征提取后的车牌图像采用连通区域质心的方法对车牌进行定位,最终得到车牌的准确区域.实验结果表明,该方法能够实现车牌的快速准确定位,是一种有效的车牌定位方法.